6533b83afe1ef96bd12a709f
RESEARCH PRODUCT
Predictive control of convex polyhedron LPV systems with Markov jumping parameters
Peng ShiFei LiuYanyan YinHamid Reza Karimisubject
convex polyhedronMarkov chainlinear parameter varying systemsLinear systemMathematicsofComputing_NUMERICALANALYSISLinear matrix inequalityOptimal controlModel predictive controlControl theoryConvex polytopeConvex optimizationMarkov jumping parametersInvariant (mathematics)predictive controlMathematicsdescription
The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon, stability of the system is guaranteed by searching a state feedback control law. Finally, receding horizon predictive controller is designed in terms of linear matrix inequality for such system. Simulation example shows the validity of this method. Refereed/Peer-reviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-05-01 | 2012 24th Chinese Control and Decision Conference (CCDC) |